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iprally
Data Scientist
June 28, 2026 – Present
projekt-zaawansowane-technologie-internetowe
June 27, 2022 – June 28, 2022
projekt-zaawansowane-technologie-internetowe — GitHub repository
View Projectdgl-dataloader-seed
April 20, 2022 – April 20, 2022
dgl-dataloader-seed — GitHub repository
View ProjectPCAPass
January 31, 2022 – February 9, 2022
This repository contains the official PCAPass implementation from the Dimensionality Reduction Meets Message Passing for Graph Node Embeddings (arXiv:2202.00408).
View Projectogbn-mag-dim-reduction
October 26, 2021 – October 26, 2021
ogbn-mag-dim-reduction — GitHub repository
View Projectfull-graph-mini-batch-convergence
July 9, 2021 – December 16, 2021
full-graph-mini-batch-convergence — GitHub repository
View Projectswarm-torch
April 23, 2021 – April 23, 2021
Optimizer for PyTorch that enables to train any PyTorch model without gradients using Particle Swarm Optimization.
View ProjectGCN
February 11, 2021 – February 12, 2021
Implementation of GCN model in PyTorch which is trained on synthetic dataset of 200 social network users to classify them into customers of Premium or Junk dog food.
View ProjectAntColonyOptimization
September 10, 2020 – December 5, 2020
The Ant Colony Optimization algorithm is a probabilistic technique for searching the shortest path. Multi-agent system of artificial ants is inspired by real ants and their pheromone based behavior.
View ProjectCultural Fit Analysis
The candidate's projects demonstrate a strong interest in advanced machine learning and optimization algorithms, which aligns well with a research-oriented or innovative data science culture. However, the projects are predominantly personal, and there is limited information on collaborative work or diverse project types beyond core ML research, which might indicate a need for further assessment regarding team integration and broader business application focus.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's project descriptions are concise, but there is no information on communication style, teamwork, or problem-solving approaches in a collaborative environment.